class_docstr: Clips the target after predicting.
  
  Parameters
  ----------
  regressor
      Regressor model for which to clip the predictions.
  y_min
      minimum value.
  y_max
      maximum value.
  
  Examples
  --------
  >>> from river import linear_model
  >>> from river import preprocessing
  
  >>> dataset = (
  ...     ({'a': 2, 'b': 4}, 80),
  ...     ({'a': 3, 'b': 5}, 100),
  ...     ({'a': 4, 'b': 6}, 120)
  ... )
  
  >>> model = preprocessing.PredClipper(
  ...     regressor=linear_model.LinearRegression(),
  ...     y_min=0,
  ...     y_max=200
  ... )
  
  >>> for x, y in dataset:
  ...     model.learn_one(x, y)
  
  >>> model.predict_one({'a': -100, 'b': -200})
  0
  
  >>> model.predict_one({'a': 50, 'b': 60})
  200
code: def _wrapped_model(self):
      return self.regressor
  
file_path: river/river/preprocessing/pred_clipper.py
imports: from __future__ import annotations
  from river import base, utils
label_desc: Use image node_id 2 for calling the PredClipper obj's underlying member method code with example usage: obj._wrapped_model() and returns: self